1. Field of the Invention
The present invention relates to dialog systems and more specifically to an extended spoken language understanding module for handling frequently asked questions.
2. Discussion of Related Art
Voice-enabled applications are becoming more widespread as automatic speech recognition (ASR), spoken language understanding (SLU), dialog management (DM) and text-to-speech (TTS) synthesizers improve. These voice-enabled applications represent an evolution of traditional help desks that are currently available on the web or supported by human agents. The goals of a voice-enabled help desk include call routing to appropriate agents or departments, providing a wealth of information about various products and services, and conducting problem solving or trouble shooting.
Speech and language processing technologies have the potential of automating a variety of customer care services in large industry sectors such as telecommunications, insurance, finance, travel, etc. In an effort to reduce the cost structure of customer care services, many of these industries are depending more heavily on complex Interactive Voice Response (IVR) menus for either automating an entire transaction or for routing callers to an appropriate agent or department. Several studies have shown that the “unnatural” and poor user interfaces of such menus tend to confuse and frustrate callers, preventing the callers from accessing information, let alone obtaining, in many cases, obtaining the desired service they expect. For example, studies show that over 53% of surveyed consumers say that automated IVR systems are the most frustrating part of customer service. In one survey, 46% of consumers dropped their credit card provider and 30% of them dropped their phone company provider due to poor customer care.
The advent of speech and language technologies have the potential for improving customer care not only by cutting the huge cost of running call centers in general but also by providing a more natural communication mode for conversing with users without requiring them to navigate through a laborious touch-tone menu. This has the effect of improving customer satisfaction and increasing customer retention rate. These values, which collectively form the foundation for an excellent customer care experience, have been evident in the AT&T Call Routing “How May I Help You” service that provides national consumer services via an automated spoken dialog system.
Soon, speech and language technologies will play a more pivotal role in customer care service and in help desk applications where the objectives include call routing and accessing information, as well as solving technical problems, sales, recommendations, and trouble shooting. Many computing and telecommunication companies today provide some form of a help desk service through either the World Wide Web or using a human agent. There is an opportunity for spoken natural language interfaces to play a much bigger role in this industry.
FIG. 1 illustrates the basic components required for human-computer interactive spoken dialog systems 10. The customer 12 speaks and provides an audible voice request. An automatic speech recognition (ASR) module 14 recognizes the speech and provides the text of the speech to a spoken language understanding (SLU) module 16 that parses the natural language input into relevant information to determine the substance of the customer inquiry. A dialog manager (DM) 18 receives the information regarding what the customer asked and generates the substance of the response, which is transmitted to a language generator 20 for generating the text of the response. The response text is transmitted to a text-to-speech (TTS) module 22 for generating a synthetic voice that “speaks” the response to the customer 12.
Further, some systems that are deployed are programmed to follow a particular dialog flow to lead the customer to the proper destination or information. Often, various costumers will have common questions that are asked that perhaps may be outside the designed dialog flow. Previous systems fail to adequately and efficiently handle these kinds of frequently asked questions.
Current technologies fail to enable companies to afford generating automated help desks. Handcrafted systems require manual training, segmenting and labeling of data in preparation for the voice user interface in the particular domain of the company. The data required for handcrafted systems may comprise hours and hours of scripted dialog with humans and the computer. The scripted computer-human interactions are studied and processed in a labor-intensive manner to train the new spoken dialog service. Such systems are time-consuming and costly to build, thus effectively preventing many companies from participating and receiving the improved customer care service that can be provided.